Air Pollution During Growth:
Accounting for Governance and Vulnerability
Susmita Dasgupta*
Kirk Hamilton
Kiran Pandey
David Wheeler
World Bank
World Bank Policy Research Working Paper 3383, August 2004
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
Authors' names in alphabetical order. The authors' positions are, respectively, Senior
Economist, Development Research Group, World Bank (WB); Lead Economist,
Environment Department, WB; Senior Environmental Economist, Global Environment
Facility (GEF); and Lead Economist, Development Research Group, WB. Financial
support for this study has been provided by the World Bank's Environment Department
and Development Research Group. Our thanks to Bart Ostro and WHO colleagues for
useful comments and suggestions, and to Piet Buys, for his assistance with GIS
applications.
Abstract
New research on urban air pollution casts doubt on the conventional view of the
relationship between economic growth and environmental quality. This view holds that
pollution automatically increases until societies reach middle-income status, because poor
countries have neither the institutional capacity nor the political commitment necessary to
regulate polluters. Some policymakers and researchers have cited this model (called the
"environmental Kuznets curve" (EKC)) when arguing that developing countries should
"grow first and clean up later." However, new evidence suggests that the EKC model is
misleading because it mistakenly assumes that strong environmental governance is not
possible for poor countries. As we show in this paper, the empirical relationship between
pollution and income becomes much weaker when measures of governance are added to
the analysis. Our results also suggest that previous research has underestimated the effect
of geographic vulnerability (climate and terrain factors) on air quality. We find that weak
governance and geographic vulnerability alone can account for the crisis levels of air
pollution in many developing-country cities. When these factors are combined with
income and population effects, we have a sufficient explanation for the fact that some
cities already have air quality comparable to levels in OECD urban areas. To summarize,
our results suggest that the maxim, "grow first, clean up later" is too simplistic.
Appropriate urban growth strategies can steer development toward cities with lower
geographic vulnerability, and governance reform can reduce air pollution significantly,
long before countries reach middle-income status.
1
1. Introduction
The environmental Kuznets curve (EKC) model posits a deterministic relationship
between economic development and environmental quality.1 In the first stage of
industrialization, pollution in the EKC world grows rapidly because people are far more
interested in jobs and income than clean air and water, communities are too poor to pay for
abatement, and environmental regulation is correspondingly weak. The balance shifts as
income rises. Leading industrial sectors become cleaner, people value the environment
more highly, and regulatory institutions become more effective. Along the curve, pollution
levels off in the middle-income range and then falls toward pre-industrial levels in wealthy
societies.
Many empirical researchers have accepted the basic tenets of this deterministic
model, and have focused on measuring its parameters. Their regressions, fitted to cross-
sectional observations across countries or regions, typically suggest that air and water
pollution increase with development until per capita income reaches a range of $5,000 to
$8,000. When income rises beyond that level, pollution starts to decline. In developing
countries and donor institutions, some policy-makers have interpreted such results as
conveying an important message about priorities: Grow first, then clean up.
If the EKC model is correct, the environment prospects are extremely poor for many
developing countries. According to the most recent World Bank estimates, average per
capita GDP in 2002 was $449 in 59 low-income countries and $1,786 in 52 lower-middle
income countries.2 These countries are nowhere near the maximum pollution point on the
conventional EKC curve so, in this model, they are fated to endure rising pollution and
1 Kuznets' name was apparently attached to the curve by Grossman and Krueger (1993), who noted its
resemblance to Kuznets' inverted-U relationship between income inequality and development.
2 GDP per capita in constant $US 1995.
2
natural resource degradation for many decades. Moreover, empirical research suggests that
pollution costs are already quite high. For example, recent World Bank estimates of
mortality and morbidity from urban air pollution in India and China suggest annual losses in
the range of 2-3% of GDP (Pandey, et al., 2004).
Should we believe this model? In fact, numerous critics have challenged the EKC,
both as a representation of what actually happens in the development process and as a policy
tool. Some critics argue that the EKC is actually too optimistic. Over time, they claim, the
curve will rise to a horizontal line at maximum existing pollution levels, as globalization
promotes a "race to the bottom," poor countries become pollution havens, and
environmental standards collapse in industrial countries as they defend their competitive
position.3
The pessimists' claims have not been bolstered by much empirical research. In fact,
recent empirical work has fostered an optimistic critique of the conventional EKC. The new
results suggest that the curve is actually flattening and shifting to the left, as growth
generates less pollution in the early stages of industrialization and pollution begins falling at
lower income levels.4 Such work, however, continues to reinforce the deterministic
worldview of the EKC model.
3 Daly (2000) has forcefully defended the view that trade and investment are affected by pollution havens.
At a union convention in 1999, Congressman David Bonior offered the following critique of the World
Trade Organization (WTO): "The WTO, as currently structured, threatens to undo internationally
everything we have achieved nationally - every environmental protection, every consumer safeguard, every
labor victory" (Bonior 1999). In a similar vein, the Nader-for-President 2000 campaign characterized the
North American Free Trade Agreement as follows: "Such one-dimensional monetized logic tramples long-
standing efforts around the world­some very successful­to protect the environment because environmental
safeguards are very often considered 'non-tariff barriers to trade' and thus become targets for removal."
(Nader 2000). Critics of trade liberalization have also raised the prospect of agricultural pollution havens,
where low-cost production with unregulated pesticide use poisons agricultural workers, as well as
consumers in countries that import their contaminated produce (Sagaris 1999; and Rauber 1997).
4 See Dasgupta, et al. (2000).
3
In this paper, we present new evidence on pollution and development which casts
doubt on the EKC approach. Our research suggests that most EKC models are
misleading because they exclude two important factors that have been hard to quantify:
governance and vulnerability to environmental damage. New evidence on governance
suggests that the EKC's deterministic link between income and policy is simply wrong:
Some poor countries have strong policy performance, and some middle-income countries
are weak in this dimension. As we will demonstrate in this paper, governance has strong,
independent effects on environmental quality. Since governance is positively correlated
with income, exclusion of governance from EKC regressions automatically inflates the
importance of income as a determinant of pollution.
The other frequently-excluded factor is local vulnerability to environmental
damage. Conceptually, the basic EKC model posits a world of homogeneous economies
operating on the same featureless plain. In reality, environmental outcomes can be
significantly affected by the sectoral composition of economic activity, as well as the
geographic features of each locus of activity. Using newly-available data, we incorporate
these factors into a more complete model of environmental change.
The remainder of the paper is organized as follows. Section 2 reviews recent
research on economy-environment links, with a particular focus on pollution. Sections 3
and 4 introduce new evidence on factors that have often been excluded from EKC
research: governance, local vulnerability and the sectoral composition of economic
activity. In section 5, we incorporate these factors into an econometric analysis of the
most recent international evidence on air pollution. Section 6 employs simulation to
4
explore the implications of our findings, and Section 7 provides a summary and
conclusions.
2. Theoretical and Empirical Work on the EKC5
Theoretical papers on the EKC have derived transition paths for pollution, abatement
effort and development under alternative assumptions about social welfare functions,
pollution damage, the cost of abatement, and the productivity of capital. This research has
shown that an inverted-U EKC can arise under the following conditions: As income
increases in a society, the marginal utility of consumption is constant or falling; the disutility
of pollution is rising; the marginal damage of pollution is rising; and the marginal cost of
abating pollution is rising. Most theoretical models do not incorporate governance quality,
since they implicitly assume the existence of a public agency that regulates pollution with
full information about the benefits and costs of pollution control. They seldom incorporate
variations in vulnerability to environmental damage, and they generally assume that the
pollution externality is local, not cross-border. In the latter case, there would be little local
incentive to internalize the externality.
Lopez (1994) demonstrates that if producers pay the social marginal cost of
pollution, then the relationship between emissions and income depends on the properties of
technology and preferences. Under homothetic preferences, an increase in output will result
in an increase in pollution. If preferences are nonhomothetic, however, the response of
pollution to growth will depend on the degree of relative risk-aversion and the elasticity of
substitution in production between pollution and conventional inputs.
5 This section draws heavily on Dasgupta, et al. (2002).
5
Selden and Song (1995) derive an inverted-U curve for the relationship between
optimal pollution and the capital stock, assuming that optimal abatement is zero until a
given capital stock is achieved, and that it rises thereafter at an increasing rate. John and
Pecchenino (1994), John et al. (1995), and McConnell (1997) derive similar inverted-U
curves by using overlapping generations models. In an interesting departure that has
particular significance for this paper, Lopez and Mitra (2000) analyze the effect of
governance on environmental quality. Their theoretical results show that for any level of
per capita income, the pollution level corresponding to corrupt behavior is always above the
socially optimal level. Further, they show that the turning point of the environmental
Kuznets curve takes place at income and pollution levels above those corresponding to the
social optimum.
Numerous empirical studies have tested the EKC model. Most have regressed
cross-country measures of ambient air and water quality on various specifications of income
per capita. These studies often rely on air pollution data from the Global Environmental
Monitoring System (GEMS), an effort sponsored by the United Nations. Stern, et al. (1998)
have supplemented the GEMS data with a more detailed accounting of airborne sulfur
emissions. Although greenhouse gases have not been included in the GEMS database,
carbon dioxide emissions estimates for most developed and developing countries are
available from the U.S. Oak Ridge National Laboratories (Marland, Boden and Andres,
2001).
Empirical researchers are far from agreement that the EKC provides a good fit to the
available data, even for conventional pollutants. In a review of the empirical literature,
Stern (1998) argues that the evidence for the inverted-U relationship applies only to a subset
6
of environmental measures; for example, air pollutants such as suspended particulates and
sulfur dioxide. Since Grossman and Krueger (1993) find that suspended particulates decline
monotonically with income, even Sterns' subset is open to contest. In related work, Stern, et
al. (1998) find that sulfur emissions increase through the existing income range. Results for
water pollution are similarly mixed. Even without incorporating other factors, then, the
EKC seems to provide an uncertain basis for understanding environmental quality changes
in developing countries.
3. Environmental Governance
Although theoretical and empirical work on the EKC has largely ignored
governance, attempts to measure its effectiveness are now well-advanced. For example,
the World Bank has committed itself to an annual, quantitative assessment of country
policies and institutional capacity for environmental governance.6 The World Bank's
CPIAE (Country Policy and Institutional Assessment for Environment) rates countries
from 1 to 6, in ascending order of effectiveness in environmental governance. Table 1
tabulates the most recent CPIAE ratings by income group for 134 developing and newly-
industrialized countries. As the table shows, the CPIAE rises moderately with income,
from a mean rating of 2.9 for low-income countries to 4.2 for upper-middle-income
countries. However, the detailed tabulation of ratings indicates the actual degree of
dispersion: low-income countries vary from 1 to 4.5; lower-middle income countries
from 2.5 to 4.5, and upper-middle income countries from 2.5 to 6.7
6 For related work at other institutions, see Esty and Cornelius (2002).
7 For 136 countries, bivariate linear and log regressions of the environmental institutions rating on GDP
per capita yield R2's of only .32 and .29, respectively.
7
Table 1: Distribution of Institutional Capacity Rating
By World Bank Income Group
No. of Mean Capacity Rating for
Income Group Countries Rating Environmental Institutions
1 2.5 3 3.5 4 4.5 5 6
% by Performance Class
Low income 58 2.89 5 29 43 19 2 2 0 0
Lower middle
income 49 3.41 0 10 27 39 20 4 0 0
Upper middle
income 27 4.24 0 4 15 15 26 7 19 15
Total 134 3.35 2 17 31 25 13 4 4 3
This dispersion may have two sources. On the "supply side," the effectiveness of
environmental institutions may reflect overall institutional effectiveness, which may in
turn reflect a variety of social and political factors that correlate only roughly with
development. On the "demand side," countries with more serious environmental
problems may devote more resources to environmental institutions, given their levels of
income and overall institutional effectiveness. In any case, the ratings in Table 1 suggest
an important message: Low levels of development do not prevent countries from having
effective environmental institutions and policies.
4. Vulnerability to Pollution
4.1 Geographic Factors
This paper focuses particularly on suspended particulate matter created by
combustion and other processes, since inhalation of these particles creates much of the
human health damage attributed to air pollution.8 Intuitively, it seems likely that once
small particles are emitted, they will stay airborne for shorter periods in areas that are
rainy and windy. More subtle factors (temperature, sunshine, air pressure, surrounding
8For a review of the evidence, see Pandey, et al. (2004).
8
terrain) may also affect the airborne suspension of particulates. Using information from
thousands of air-quality monitoring reports, a recent World Bank ­ WHO study has
provided the first systematic quantification of these factors (Pandey, et al, 2004). The
study has combined their estimated impacts into a vulnerability index for approximately
3,200 world cities whose population exceeds 100,000.
The results suggest great variation in the atmospheric impact of fine-particulate
emissions. Across cities, the 1st-and 99th-percentile index values are 15 and 83,
respectively. By implication, the impact of particulate emissions varies more than five-
fold from cities with the least natural vulnerability to those with the most. Vulnerability
is highly varied, both within and across regions: All continents have regions of low and
high vulnerability.
Table 2: World Urban Population (Millions)
By Geographic Vulnerability and Income
World Bank
Vulnerability Income Group Total
Low Middle High
0-20 8 25 12 45
21-40 93 193 194 480
41-60 133 627 293 1,053
61-80 150 169 32 351
81-100 50 0 0 50
Total 434 1,014 530 1,978
Table 2 provides a breakdown of world urban population by atmospheric
vulnerability and income group. In low-income countries, approximately 100 million
people live in areas whose climates and terrain features indicate low vulnerability (0-40),
130 million live at medium vulnerability (41-60), and 200 million at high vulnerability
(61-100). In high-income countries, by contrast, most people live under conditions of
low or medium vulnerability. We emphasize that, given the population distribution, these
9
conditions are simply given by nature: In high-vulnerability cities (and
disproportionately in low-income countries), a unit of particulate emissions pollutes the
air much more than in low-vulnerability cities.
4.2 Economic Structure
For economic-environmental analysis, measures of aggregate economic output can
be very misleading because the composition of output is critical for understanding
potential environmental impacts. To illustrate, consider the impact of industrial
development on air and water pollution. Intuitively, it seems clear that not all industry
sectors are equal sources of environmentally damaging emissions: A shirt factory is not a
steel mill. In recent years, we have been able to quantify the dimensions of this
difference by industry sector, for a large number of pollutants.9 We have found that of
twenty-eight industry sectors coded at the 3-digit international classification (ISIC), only
seven persistently account for at least 90% of global emissions for major air and water
pollutants. Table 3 displays estimated percent contributions by sector, pollutant and
decade. Besides indicating the large aggregate contribution, it indicates even more
sectoral concentration for individual pollutants, along with a high degree of stability
during the past four decades.10
Comparative advantage and government policies have both affected the
international distribution of industrial activity during the past several decades.
9 For a detailed discussion, see Hettige, et al. (1995).
10For the derivation of emissions estimates, see Hettige, Mani and Wheeler (2000).
10
Table 3: Percent Contribution to Global Industrial Emissions:
Seven Industry Sectors
Non-
Industry Iron & Petroleum Food Industrial Paper & Ferrous
Sector Steel Refineries Products Chemicals Products Metals Cement
(ISIC Code) (371) (353) (311) (351) (341) (372) (369) Total
Air Pollutants
Particulate Air
Pollution
(PM-10)
1960 29.0 1.4 8.5 1.6 2.2 0.7 52.5 96.0
1970 27.6 1.4 8.5 1.8 2.0 0.7 53.9 96.0
1980 25.5 1.5 8.6 2.0 1.9 0.7 55.8 96.0
1990 21.8 1.0 7.9 1.8 2.0 0.7 60.3 95.4
Sulfur Dioxide
1960 18.4 25.2 3.2 10.2 6.9 13.0 11.4 88.4
1970 17.6 25.2 3.2 11.2 6.4 12.9 11.8 88.3
1980 15.8 25.3 3.1 12.7 5.8 13.3 11.9 88.0
1990 15.3 19.5 3.2 12.3 6.8 15.0 14.5 86.6
Toxic Chemicals
1960 5.8 6.8 0.7 36.6 7.3 5.7 1.5 64.4
1970 5.3 6.6 0.7 38.9 6.5 5.4 1.5 65.0
1980 4.6 6.4 0.7 42.7 5.7 5.4 1.5 67.0
1990 4.3 4.8 0.7 40.1 6.5 5.9 1.7 64.1
Water Pollutants
Biochemical
Oxygen
Demand
1960 0.1 2.4 32.7 21.7 28.2 7.5 0.1 92.7
1970 0.1 2.4 32.9 23.8 25.8 7.4 0.1 92.5
1980 0.1 2.4 32.1 26.9 23.2 7.6 0.1 92.4
1990 0.1 1.7 31.2 24.5 25.9 8.1 0.1 91.6
Toxic Chemicals
1960 10.8 2.7 1.1 67.5 9.7 1.2 0.1 93.1
1970 9.8 2.6 1.0 70.5 8.5 1.1 0.1 93.6
1980 8.2 2.4 0.9 74.5 7.1 1.1 0.1 94.4
1990 8.0 1.9 1.0 73.0 8.6 1.2 0.1 93.8
11
The resulting differences have significant environmental implications: Industrial
economies focused on light, labor-intensive assembly (e.g., apparel, electronics,
furniture) are far less susceptible to pollution than those with a heavy concentration of
activity in the seven "dirty" industry sectors. Rapid growth in the first group may have
very modest environmental impacts, while growth in the second may endanger thousands
of lives annually.
5. The EKC Revisited: Accounting for Governance and Vulnerability
For our empirical work, we focus on one form of air pollution, suspended
particulate matter (TSP), because suspended particulates have significant health impacts,
and TSP data are available for many developing countries. We compare results for a
conventional EKC model, in which the atmospheric concentration of TSP is a function of
income per capita alone, and an extended model that includes measures of governance,
geographic vulnerability, and the pollution-intensity of industrial activity. In addition, we
include two controls for city size: Population, which proxies the scale of local pollution-
generating activities, and population density, which proxies the space-intensity of the
same activities. We also allow for the possibility of an exogenous trend in pollution,
reflecting the international diffusion of cleaner technology and (perhaps)
environmentalist values during the 1990's.
We employ the latest available TSP dataset from WHO, which includes time series
from 1986 to 1999 for 340 individual air quality monitors in 170 cities. Of these, 209
monitors and 85 cities are in developing, newly-industrialized or Eastern European
12
countries. For exact comparison over time, we match air quality measures from specific
monitors in different periods.11
We draw our measures of country income per capita and city population from
World Bank and UN databases. We have computed city population densities using a
Geographic Information System (GIS), by overlaying a fine-grid map of world
population distribution on standard circles whose 20-km. radii extend from geographic
center points for the cities in our sample. We also use the city-specific geographic
vulnerability indices and shares of the seven "dirty" industry sectors that we introduced
in Section 4.
Our panel data span 14 years, and we cannot depend on recently-computed World
Bank CPIAE ratings to proxy environmental governance since the mid-1980's. The only
panel data available for the entire period are country corruption indices published by
Transparency International (TI). TI's corruption index for 2003 is highly correlated with
the 2003 CPIAE: The linear regression result is CPIAE = 2.06 (10.2) + 0.48 TI (8.0); R2
= .41 N = 92 (t-statistics in parentheses). We therefore employ the TI corruption index
for 1986-1999 as our governance proxy.12
We compute average values of regression variables for three periods: 1986-90,
1991-95 and 1996-99. Table 4 presents conventional EKC estimates for the three
periods, with and without monitor site matching across periods, along with pooled panel
11 Air quality measures can differ substantially across monitors within cities, and across cities within
countries. Changing site composition can introduce both random error and systematic bias into
intertemporal comparisons using city or country averages, the latter because initially-monitored sites tend
to be more polluted than later additions.
12 Since the TI index reflects perceived corruption, it also provides evidence for testing the previously
mentioned corruption-pollution hypothesis of Lopez and Mitra (2000).
13
estimates for all three periods.13 Table 6 presents period and panel estimates for the full
set of hypothesized TSP determinants. All regressions are in log form.
Table 4a reports linear and quadratic estimates for the simple EKC model, using
non-matched monitoring data to maximize degrees of freedom in each period. The
results in 4a are consistent with the findings in Grossman and Krueger (1993). Across
the three sample periods, they suggest a monotonically-declining relationship between
income and air pollution.14 The estimated elasticity of TSP with respect to income per
capita is around -0.4 in all three periods, with very high t-statistics and high regression
R2's. The results suggest that air pollution declines approximately 0.4% with each 1%
increase in income per capita.
Tables 4b and 4c use a panel of matched monitoring sites, to preserve comparability
across periods. Table 4b replicates the period estimates in 4a, and shows that the
matched sample fit is very close to the full sample fit in each period: Estimated
elasticities are around -0.4, t-statistics are very high, and regression R2's are around 0.60.
Table 4c presents panel estimates for the pooled sample of matched sites, enabling us to
estimate the overall EKC relationship with maximum degrees of freedom. For the OLS
and random effects models, the estimated elasticities are again around -0.4 and the
regression fit appears quite robust. Our results for the period dummies suggest a
significant trend in the matched sample: Compared with TSP concentrations in
13 Degrees of freedom are greater without site matching, since site measures are not always available for
all three periods. However, as we noted previously, site matching assures perfectly comparable results
across periods.
14 This is true even for the quadratic regression for 1986-90, which has a significant quadratic term but an
insignificant linear term. The scatter of TSP concentration on income for this period is downward-sloping
throughout, with some indication of a steeper slope at higher income levels. However, even this apparent
relationship disappears in the data (and regression results) for the 1990's.
14
Table 4: Conventional EKC Estimates
Dependent variable: Log TSP (ug/m3)
a. Monitoring Sites Not Matched
(1) (2) (3) (4) (5) (6)
86-90 86-90 91-95 91-95 96-99 96-99
Log GDPPC 0.482 -0.367 -0.142 -0.434 0.148 -0.424
(1.83) (17.39)** (0.51) (20.38)** (0.45) (17.86)**
[Log GDPPC]**2 -0.054 -0.018 -0.034
(3.24)** (1.05) (1.76)
Constant 4.550 7.747 7.094 8.226 5.717 8.018
(4.54)** (43.96)** (6.50)** (46.80)** (4.33)** (39.28)**
Obs 192 192 241 241 236 236
R-squared 0.63 0.61 0.64 0.63 0.58 0.58
b. Period Regressions: Monitoring Sites Matched
86-90 91-95 96-99
Log GDP Per Capita -0.342 -0.401 -0.417
(11.99)** (15.18)** (13.65)**
Constant 7.526 7.879 7.961
(31.65)** (35.80)** (30.97)**
Observations 122 122 122
R-squared 0.54 0.66 0.61
c. Balanced Panel Regressions (Monitoring Sites Matched)
Random Fixed
OLS Effects Effects
Log GDP Per Capita -0.386 -0.364 0.035
(23.38)** (13.65)** (0.32)
Period Dummy, 91-95 -0.124 -0.124 -0.131
(1.85) (4.44)** (4.74)**
Period Dummy, 96-99 -0.174 -0.176 -0.220
(2.60)** (6.28)** (7.34)**
Constant 7.881 7.699 4.447
(55.24)** (34.60)** (4.95)**
Observations 366 366 366
R-squared 0.61 .61 .11
Within Groups .17 .21
Between Groups .63 .63
Number of groups(monitors) 122 122
Absolute value of t statistics in parentheses
significant at 5%; ** significant at 1%
15
1986-90 (ceteris paribus), overall concentrations are .12 log units lower in 1991-95 and
.18 log units lower in 1996-1999.
The fixed-effects results in the third column of Table 4c introduce controls for all
122 monitoring sites in the data. By removing the influence of cross-sectional variation
in the sample, they provide evidence on marginal (within-site) relationships. Although
the international diffusion effects are replicated here, the result for income obviously tells
a very different story. Table 5 shows that during the study period, the monitoring-
sample countries experienced widely-varying changes in income per capita.
Table 5: Statistics for Per Capita Income Growth:
Sample Monitoring Sites
Minimum 1st Quartile Median 3rd Quartile Maximum
-36.0 5.7 12.3 42.4 68.1
Despite this wide variation, the fixed-effect results indicate no marginal responsiveness
of air pollution to income. The estimated elasticity is only 0.04, and it is statistically
insignificant.
Table 6 displays estimates for our full model, which includes per capita income,
governance, geographic vulnerability, urban population, population density, and
pollution-intensive sector share. Cross-sectional relationships are captured in the period
regressions, and in the OLS and random-effects regressions on the pooled sample. All of
these results tell essentially the same story about average relationships in the data. The
governance effect is large and highly significant in all cases. In the pooled random
effects model, for example, urban TSP concentration declines about -.60% for each 1%
improvement in the Transparency International governance index. Similar results hold
for the geographic vulnerability index, which is highly significant in all cases. In the
16
Table 6: Determinants of Urban TSP Concentrations
(Matched Monitoring Sites)
Dependent Variable: Log TSP (ug/m3)
Period Regressions
(1) (2) (3)
86-90 91-95 96-99
Log GDP Per Capita -0.007 0.131 -0.046
(0.19) (1.59) (0.61)
Log TI Governance Index -0.535 -0.862 -0.783
(4.23)** (4.31)** (3.15)**
Log Vulnerability Index 1.087 1.267 0.744
(7.54)** (6.99)** (3.51)**
Log City Population -0.026 -0.067 -0.020
(0.46) (1.21) (0.34)
Log City Pop. Density 0.182 0.227 0.092
(3.63)** (4.48)** (1.53)
Log Pollution-Intensive 0.245 1.168 0.469
Sector Share (1.79) (3.16)** (1.96)
Constant 0.635 1.210 3.654
(0.59) (1.19) (3.30)**
Observations 82 82 103
R-squared 0.85 0.87 0.80
Panel Regressions
Random Fixed
OLS Effects Effects
Log GDP Per Capita -0.034 -0.119 -0.472
(1.14) (3.22)** (2.46)*
Log TI Governance Index -0.686 -0.588 -0.349
(7.25)** (5.62)** (2.87)**
Log Vulnerability Index 0.926 0.794
(9.70)** (5.34)**
Log City Population -0.049 -0.019
(1.49) (0.39)
Log City Pop. Density 0.156 0.103
(4.97)** (2.06)*
Log Pollution-Intensive 0.399 0.038
Sector Share (3.61)** (0.42)
Period Dummy, 91=95 -0.080 -0.116 -0.095
(1.34) (3.77)** (3.02)**
Period Dummy, 96-99 -0.072 -0.159 -0.086
(1.26) (4.64)** (1.80)
Constant 2.522 2.979 9.074
(4.29)** (3.50)** (4.77)**
Observations 267 267 322
R-squared 0.83 .82 .66
Within Groups .30 .24
Between Groups .82 .62
Number of groups(monitors) 118 122
Absolute value of t statistics in parentheses
Significant at 5%; ** significant at 1%
17
random effects model, TSP concentration increases about .8% for each 1% increase in
vulnerability. The results for pollution-intensive sectors are more varied, although the
coefficients are always positive, and highly significant in two of five cases. Population
does not enter significantly, but population density is significant in four of five equations.
The period dummies are negative and significant in the random effects equation, with
magnitudes similar to those in the simple EKC results.
For this paper, the most important result is the reduced role of per capita income in
the more fully-specified model. Income loses all significance in four of the five cross-
sectional regressions, and even where it is significant (in the random effects equation), its
estimated elasticity is about one-fourth of its value in the conventional EKC estimates. In
the cross-sectional model, the strength of the EKC relationship is substantially reduced
by the inclusion of governance, geographic vulnerability, sectoral pollution-intensity and
urban population. Governance and vulnerability seem to be particularly important
factors.
Again, however, the fixed-effects estimates in Table 6 tell a different story at the
margin. Since the fixed-effects model introduces site-specific controls, we cannot
estimate separate effects for two fixed factors ­ vulnerability and population density.
However, we can assess the impact of marginal changes in income and governance at the
monitoring sites in our sample. Once we control for governance, income re-emerges as a
significant factor: The estimated income elasticity of air pollution is about -.50, and
significant at the 5% level. At the margin, the fixed-effect result suggests that urban TSP
concentration declines about .5% for each 1% increase in per capita income. The
estimated effect for governance remains highly significant, with a somewhat smaller
18
magnitude than in the five cross-sectional equations. The period diffusion effects are
smaller in the fixed-effect result, and the significance of the late-90's effect is lower.
To summarize, in cross-sectional EKC regressions, inclusion of governance,
vulnerability and other variables drastically alters (indeed, effectively eliminates) the
conventional EKC relationship. In the fixed-effects model, on the other hand, controlling
for governance changes the estimated marginal income elasticity of TSP concentration
from negligible (in Table 4) to quite strong (Table 6). At the same time, the importance
of governance is suggested by both the cross-sectional and fixed effects results.
6. Determinants of Urban Air Pollution: Present and Future
Using our econometric results, we perform two sets of simulation experiments to
assess the relative impact of income, governance, vulnerability and population on urban
air pollution. For the simulations, we have re-estimated the regressions after dropping
insignificant variables.
6.1 Comparative Impacts
In the first set of experiments, we use the random effects model to predict air
pollution levels for cities whose characteristics represent low, medium and high values of
the four determinants. Table 7 displays the relevant ranges from the sample dataset.
Table 7: Simulation Range for Random Effects Results:
Characteristics of Non-OECD Cities
Income per TI Locational Population
Capita Governance Vulnerability Density
($US) Index Index (/sq. km.)
Low 250 1.5 15 100
Medium 1,000 4.5 50 5,000
High 8,000 7.5 85 40,000
19
To assess the partial effect of each determinant, we establish the baseline TSP
concentration for a city with "worst-case" conditions (income $250, governance 1.5;
vulnerability 85, population density 40,000/sq. km.). For the period 1996-99, the random
effects equation predicts an airborne TSP concentration of 437 ug/m3 for this case ­
approximately nine times the current median TSP concentration for OECD cities. From
the baseline, we use the random effects equation to predict the partial effect as the four
determinants are increased to medium and high levels. Table 8 presents the results.
Table 8: Partial Impacts on TSP Levels
Index Locational Population
Level Income Governance Advantagea Dispersionb
Low 437 437 437 437
Medium 350 280 302 342
High 252 228 131 216
aMeasured as declining vulnerability index values (85 => 50 => 15)
bMeasured as declining densities (40,000 => 5,000 => 100)
For consistent interpretation in Table 8, we invert the measures of geographic
vulnerability (to "locational advantage") and population density (to "population
dispersion"). Among the four determinants, locational advantage clearly has the greatest
impact across its range in the sample data. Holding income, governance, and population
density constant at "worst-case" levels, changing locational advantage from low to high
reduces predicted air pollution from 437 to 131 ug./m3. This result suggests that
geographic factors alone are sufficient to determine whether a poor, overcrowded,
poorly-governed city will suffer from crisis-level air pollution, or experience pollution
near the upper bound for air pollution in OECD cities (see Table 11). The other three
determinants have major impacts on the TSP concentration, with similar orders of
20
magnitude in the transition from low to high values. TSP falls from 467 to 252 for
income, to 228 for governance, and to 216 for population dispersion.
Table 9 adds another perspective, by measuring the joint impacts of TSP
determinants. For jointly low, medium and high values, governance and locational
advantage reduce TSP from 437 to 194 and 69. In combination, all four determinants
reduce TSP from 437 to 122 and 20 for medium and high values. Thus, the econometric
evidence suggests that air pollution near the OECD median could be attained by a
developing-country city with excellent governance and strong locational advantage.
When all four determinants take on high values, simulated TSP falls to a level well below
the OECD median.
Table 9: Joint Impacts on TSP Levels
TSP Determinants:
Joint Effect
Joint Index Governance Income, Governance,
Values + Location Location, Pop. Density
Low 437 437
Medium 194 122
High 69 20
We should stress that these results are not artifacts of our regressions. They reflect
actual patterns of variation in the urban air quality data. The comparative statistics in
Table 11 show that many non-OECD cities already have air quality that falls within the
current range of OECD TSP concentrations (15-109). Our econometric analysis has
suggested why this should be the case: Despite high levels of poverty and frequent
crowding, many non-OECD cities have benefited from good governance and locational
advantages that have given them low vulnerability to air pollution. Nevertheless, as the
distributional statistics for 1996/99 make clear, at least 75% of non-OECD cities
21
currently have TSP concentrations that would be considered crisis levels in the OECD.
Problems with governance and vulnerability explain a major part of this disparity.
6.2 Predictions for 2025
In the second set of experiments, we predict future TSP levels under alternative
assumptions about the time paths of three pollution determinants in our sample cities. In
the baseline experiment, we use trends during the 1990's in each city to forecast income,
governance and population in 2025. With these forecast values, we predict TSP levels in
2025 for both random and fixed-effects models. In the "policy reform" experiment, we
assume that real per capita income grows at a 5% annual rate in each sample country; the
governance index reaches the current 25th-percentile value for OECD countries; and the
growth rate of the urban population is one-half of the actual rate during the period 1995-
2000. Figures 1a and 1b display the results for the random and fixed-effects models, and
Table 11 summarizes the same information, along with comparative statistics for OECD
cities in the sample dataset.
The baseline prediction reflects the assumption that recent trends at each monitoring
site will continue through 2025. As Table 10 shows, not all of these trends are favorable.
Table 10: Trends in Determinant Values
1st 3rd
Minimum Quartile Median Quartile Maximum
Per Capita Income: % Change, -58.3 -12.1 7.8 42.4 117.7
1986/90 ­ 1996/99
TI Governance Index: Change, -2.4 -1.9 -0.5 0.4 1.9
1986/90 ­ 1996/99
City Population: % Change, -7.1 1.9 13.0 15.0 26.7
1995-2000
According to the TI index, over half the sample non-OECD sites were in countries with
declining governance quality in the 1990's. While income grew rapidly in some
22
countries, it also deteriorated markedly in others, and the median real income gain (7.8%
for nearly a decade) was quite modest. Urban population continued to grow rapidly in
many sample countries, with a median increase of 13% during the period 1995-2000.
Under these conditions, we would expect a mixed set of site-level baseline projections for
TSP through 2025. A positive element in our regression-based model is the secular trend
downward in overall air pollution from the diffusion of clean technology and
environmentalism. We assume that this trend will continue until 2025.
Under the baseline assumptions, Table 11 shows that our random effects predictions
anticipate very modest improvements through 2025, and the fixed-effects predictions are
somewhat more optimistic. Across the sample non-OECD cities, the median TSP level
falls from 161 to 133 in the random-effects results, and to 119 in the fixed-effects results.
The difference is more noticeable in the upper tail of the distribution: The maximum
level changes little for random effects, but declines from 470 to 378 for fixed effects. For
either model, the baseline predictions are still far from current OECD levels by 2025.
In the policy reform scenario, however, conditions improve markedly. Median TSP
falls to 91 for random effects and 71 for fixed effects ­ both within the current range for
OECD cities. Maximum levels also fall markedly (to 248 and 193, respectively), and in
the fixed-effects case, 75% of non-OECD cities have reached the current OECD range by
2025. This prediction reflects assumptions that, while optimistic, are by no means out of
reach for many developing countries. We conclude that, despite the geographic
vulnerability of many developing-country cities, a quarter century of sustained growth
and governance reform could bring them within range of the air quality currently enjoyed
by most people in the OECD countries.
23
Table 11: Historical and Predicted TSP Levels, 1986 ­ 2025 (Percentiles)
Non-OECD Cities: Historical Statistics
Minimum 10% 25% Median 75% 90% Maximum
Non-OECD TSP, 1986-90 59 76 133 220 345 503 560
Non-OECD TSP, 1991-95 45 74 112 214 329 464 728
Non-OECD TSP, 1996-99 9 59 82 161 256 368 470
Random Effects Predictions
Non-OECD TSP 2025 (Baseline) 10 49 70 133 245 373 464
Non-OECD TSP2025 (Reform) 4 31 51 91 136 193 248
Fixed Effects Predictions
Non-OECD TSP 2025 (Baseline) 9 46 70 119 195 272 378
Non-OECD TSP2025 (Reform) 3 24 39 71 105 149 193
OECD Cities: Historical Statistics
OECD TSP, 1986-90 23 38 42 58 83 122 206
OECD TSP, 1991-95 21 28 36 49 62 95 136
OECD TSP, 1996-99 15 24 32 42 49 64 109
7. Summary and Conclusions
In this paper, we have revisited the environmental Kuznets curve for air pollution,
using new data on the determinants of air quality. Using a balanced panel of air
monitoring data for the period 1986-1999, we have estimated air quality models that
control for governance, vulnerability, population density and pollution-intensive
economic activity, as well as income per capita. Our econometric results show varied
impacts for income, but they are unambiguous in their assignment of importance to
governance and geographic vulnerability. Using both random- and fixed-effects
estimators, we use simulation experiments to assess the relative importance of income,
governance, vulnerability and population density as determinants of air quality. We find
that governance and geographic vulnerability alone are enough to account for the crisis
levels of air pollution in many developing-country cities. When their effects are
combined with those of income and population density, we have a sufficient explanation
24
for the fact that some developing-country cities already have air quality comparable to
levels in OECD cities.
In another simulation, we project air pollution in 2025 for our sample cities using
two sets of assumptions. In the baseline set, we allow current trends to continue for
income, governance and population density. This leads to substantial improvement in air
quality for the fixed-effects model, and moderate improvements for the random-effects
model. In the second set, we assume that policy reforms produce real income growth of
5% annually, improved governance sufficient to achieve parity with the current lower
quartile of OECD countries, and urban population growth at half the rate observed in
1995-2000. In the reform scenario, both random- and fixed-effects models predict sharp
improvements in air quality for most developing-country cities. By 2025, 75% have
attained air quality within the current range experienced by OECD cities.
In light of these results, we believe that policy makers should be wary of the
conventional EKC model. Our results offer no support for the view that air quality
deteriorates during the first phase of economic growth. At worst, air quality remains
constant, even at very low income levels, and our fixed-effects results suggest that
income growth significantly improves air quality at the margin. Nor do our results
support the EKC-motivated view that citizens of poor countries necessarily face a long
wait for major improvements in air quality. Significantly-improved governance is
possible in poor countries, and our results suggest that policy reform alone is sufficient to
reduce air pollution by 50%, even in overcrowded, geographically-vulnerable cities in
countries with very low incomes.
25
Although our results have an optimistic cast, we feel compelled to close with some
notes of caution. Our results for geographic vulnerability suggest that increases in air
emissions are much more dangerous in some cities than in others. In light of this finding,
urban planners may want to take vulnerability into account as they consider national and
regional policies for the next round of urban development. We should also note that
nothing in our hopeful predictions is preordained. Our results imply that air quality will
become worse in cities with stagnating or falling incomes, deteriorating governance, and
rapidly-growing populations. And even in the reform scenario, improvements by 2025
cannot save many people who will die from dangerous air pollution during the next
quarter century. We see no conflict between urban air quality and economic growth (the
converse, in fact), but improved environmental governance seems to provide our best
hope for rapid improvement.
26
References
Bonior, D., 1999, "Defending Democracy in the New Global Economy," Statement to an
AFL-CIO Conference on Workers' Rights, Trade Development, and the WTO. Seattle,
Washington, December.
Daly, H., 2000, "Globalization," Presented at the 50th Anniversary Conference of the
Aspen Institute. Aspen, Colorado. August.
Dasgupta, S., Laplante, B., Wang, H., Wheeler, D., "Confronting the Environmental
Kuznets Curve," 2002, Journal of Economic Perspectives, Vol. 16, No. 1, Winter.
Esty, D., Cornelius, P., 2002, Environmental Performance Measurement: The Global
Report 2001-2002, Oxford Press.
Grossman, G., Krueger, A., 1993, "Environmental Impacts of the North American Free
Trade Agreement," in P. Garber (ed.), The U.S.-Mexico Free Trade Agreement
(Cambridge: MIT Press, 1993).
Hettige, M., Mani, M., Wheeler, D., 2000, "Industrial Pollution in Economic
Development: The Environmental Kuznets Curve Revisited," Journal of Development
Economics, 62(2), August, pp. 445-476.
John, A, Pecchenino, R., Schimmelpfennig, D., Schreft, S., 1995, "Short-Lived Agents
and the Long-Lived Environment," Journal of Public Economics, 58, 127-141
Lopez, R., 1994, "The Environment as a Factor of Production: The Effects of Economic
Growth and Trade Liberalization," Journal of Environmental Economics and
Management, 27, 163-184.
López, R., Mitra, S., 2000, " Corruption, Pollution, and the Kuznets Environment Curve,"
Journal of Environmental Economics and Management, Vol. 40, No. 2, pp. 137-150
Mani, M., Wheeler, D., 1998, "In Search of Pollution Havens? Dirty Industry in the
World Economy, 1960-1995," Journal of Environment and Development, Fall.
Marland, G., Boden, T., Andres, R., 2001, "Global, Regional, and National Fossil Fuel
CO2 Emissions," Carbon Dioxide Information Analysis Center, Oak Ridge National
Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee (available online at
).
Hettige, H., Singh, M., Martin, P., Wheeler, D., 1995, "The Industrial Pollution
Projection System," World Bank Policy Research Working Paper, No. 1431, March.
McConnell, K.E., 1997, "Income and the Demand for Environmental Quality,"
Environment and Development Economics, 2(4), 383-399.
27
Nader-for-President, 2000, http://www.votenader.com/issues/environment.html.
Pandey, K. D., Bolt, K., Deichmann, U., Hamilton, K., Ostro, B., Wheeler, D., 2004
(forthcoming), "The Human Cost of Air Pollution: New Estimates for Developing
Countries," World Bank Development Research Group Working Paper, Washington, DC.
Rauber, P., 1997, "To Every Fruit There is a Season," Sierra Club Magazine, 82 (1)
January/February.
Sagaris, L., 1999, "Not Now, NAFTA: Chile's Economy may be Ready for Free Trade,
But Its Environment is Not," Sierra Club Magazine, 84 (1) January/February.
Selden, T., Song, D., 1995, "Neoclassical Growth, the J Curve for Abatement, and the
Inverted U Curve for Pollution," Journal of Environmental Economics and Management,
29, 162-168.
Stern, D. I., Auld, A., Common, M., Sanyal, K., 1998, "Is There an Environmental
Kuznets Curve for Sulfur?" Working Papers in Ecological Economics, 9804, Center for
Resource and Environmental Studies, Australian National University, Canberra.
Stern, D. I., 1998, "Progress on the Environmental Kuznets Curve?" Environment and
Development Economics, 3, 175-198.
Wheeler, D., 2001, "Racing to the Bottom? Foreign Investment and Air Pollution in
DevelopingCountries," Journal of Environment and Development, September.
World Bank, 2003, World Development Report 2003: Sustainable Development in a
Dynamic World, Washington: World Bank/Oxford University Press.
28
Figure 1: TSP Distributions in Non-OECD Cities: 1986 ­ 2025
(1a) Random-Effects Model
600
400
200
0
TSP 86-90 TSP 91-95
TSP 96-99 Baseline TSP 2025
Reform TSP 2025
excludes outside values
(1b) Fixed Effects Model
600
400
200
0
TSP 86-90 TSP 91-95
TSP 96-99 Baseline TSP 2025
Reform TSP 2025
excludes outside values
29